Developer Diaries

Thief: Deadly Shadows - Volume II

Developer David Riegel slips out of the shadows to talk about how the AI behavior in Thief: Deadly Shadows was revamped to enhance the stealth gameplay.

[The original Thief games have always brought a suspenseful blend of stealth, action, and exploration to the gaming world. Deadly Shadows, currently under development for PC and Xbox, is looking to surpass its predecessors on every front. Last month Sound Designer Brian Sharp talked about the positional audio of the game. This month, Developer David Riegel briefs us, in his own words, on how the A.I. is being trained to ensure that the gamer has the perfect stealth experience...].

Cartwheeling Around the Core Stealth and AI Design

There were a lot of really interesting decisions that went into planning how the A.I. in Thief: Deadly Shadows were going to respond and interact with their environment. We knew early on that we were going to be writing the A.I. code from scratch, so we had an opportunity to think about the core stealth experience in Thief 1 and Thief 2, what was wrong with it, and how we could improve upon it. Thief: DS required a lot of unique features -- not present in Thief 1 and 2 -- in order to fulfill the vision of the game: non-hostile A.I., a faster and more advanced combat system, factional relationships, etc. However, our ultimate focus rested on the core stealth experience. The biggest questions on our minds were, firstly, how we could maintain stealth tension while retaining an action feel? And secondly, how we could improve A.I. spatial awareness of their surroundings during searching?

So, right away it was down to business. We spent a bunch of time looking at stealth games on the market, and, of course, analyzing Thief 1 and 2. Actually, we spent a lot of time playing other stealth games definitely the hard part of the research!

Interestingly, it was at first sometimes difficult to determine what made some stealth games work and what made some fail. We did notice trends throughout the entire genre, though. One of the things we noticed right away is that in stealth-action games the stealth and the action are often two independent features. The stealth element is the slow-paced, sneaking, tension gameplay, and the action is what happens when you get caught. The games that tended to be the most successful were the ones who were good at creating both good combat and good stealth. (We also noticed that the only game to have a stealthy nekkid dude, MGS 2, was hugely successful.)

OK, so we wanted good combat and good stealth, no problem. But how could we take the core stealth itself and make it better? Too often it's easy to create stealth that is too slow and not as satisfying as it could be. Each individual element of Thief: DS had to be strong in and of itself, and not just the combination, much like well much like two strong things. One of the elements that is fun about traditional pure stealth is that it creates moments of fantastic suspense -- lurking in darkness or hiding behind objects while an A.I. searches for you is part of the core fantasy element. And what's not fun you might ask? Well, to us it was the wait for long periods of time part. Those moments of tension created when you're hiding in shadows are pure adrenaline, waiting to see if you're going to get caught -- but after a certain amount of time that tension fades and it becomes a waiting contest. You have to wait for the A.I. to finish what it's doing before you can move again. Suspense is good, but an elevator-riding simulation we are not. One of our design goals was to keep the player tense, but not confined to a single dark space -- we wanted things moving and fluid -- at least a large part of the time.

At that point we began to consider the problem of spatial awareness. When doing searching behavior in stealth games, it's really easy to create obsessive-compulsive A.I. No, not the fun kind that wash their hands a million times and turn the oven on and off -- those are fine. I mean the kind of A.I. that walks around the same space, checks the same rooms, and generally doesn't really understand what kind of space it's in. We knew that we wanted A.I. that were more spatially aware and could tell where they'd searched versus where they hadn't.